Implementation of the Chamfer Distance as a module for pyTorch
This is an implementation of the Chamfer Distance as a module for pyTorch. It is written as a custom C++/CUDA extension.
As it is using pyTorch's JIT compilation, there are no additional prerequisite steps that have to be taken. Simply import the module as shown below; CUDA and C++ code will be compiled on the first run.
from chamfer_distance import ChamferDistance chamfer_dist = ChamferDistance()
points and points_reconstructed are n_points x 3 matrices
dist1, dist2 = chamfer_dist(points, points_reconstructed) loss = (torch.mean(dist1)) + (torch.mean(dist2))
This code has been integrated into the Kaolin library for 3D Deep Learning by NVIDIAGameWorks. You should probably take a look at it if you are working on anything 3D :)